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Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

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  • Nathan Oaks Farrar

    (Department of Electrical and Computer Engineering, The Herff College of Engineering, The University of Memphis, Memphis, TN 38111, USA)

  • Mohd Hasan Ali

    (Department of Electrical and Computer Engineering, The Herff College of Engineering, The University of Memphis, Memphis, TN 38111, USA)

  • Dipankar Dasgupta

    (Department of Computer Science, The University of Memphis, Memphis, TN 38111, USA)

Abstract

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the challenges associated with designing an efficient control scheme for wind farms; (iii) a breakdown of the different types of AI and ML algorithms used in wind farm controllers and control schemes; (iv) AI and ML for wind speed prediction; (v) AI and ML for wind power prediction; (vi) AI and ML for mechanical component monitoring and fault detection; and (vii) AI and ML for electrical fault prevention and detection. This paper will offer researchers and engineers in the wind energy generation field a comprehensive review of the application of AI and ML in the control methodology of offshore and onshore wind farms so that more efficient and robust control schemes can be designed for future wind turbine controllers.

Suggested Citation

  • Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1530-:d:1057155
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    as
    1. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    2. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    3. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
    4. Chao Fu & Guo-Quan Li & Kuo-Ping Lin & Hui-Juan Zhang, 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    5. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    6. Moreno, Sinvaldo Rodrigues & dos Santos Coelho, Leandro, 2018. "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System," Renewable Energy, Elsevier, vol. 126(C), pages 736-754.
    7. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    8. Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
    9. Peng Guo & Jian Fu & XiYun Yang, 2018. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model," Energies, MDPI, vol. 11(9), pages 1-16, August.
    10. Zexian Sun & Hexu Sun & Jingxuan Zhang, 2018. "Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-15, July.
    11. Kamal Elyaalaoui & Moussa Labbadi & Mohammed Ouassaid & Mohamed Cherkaoui, 2021. "Optimal Fractional Order Based on Fuzzy Control Scheme for Wind Farm Voltage Control with Reactive Power Compensation," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, March.
    12. Lakhdar Mazouz & Sid Ahmed Zidi & Mohamed Khatir & Tahar Benmessaoud & Slami Saadi, 2016. "Particle swarm optimization based PI controller of VSC-HVDC system connected to a wind farm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 239-246, December.
    13. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    14. Zhao, Xuejing & Wang, Chen & Su, Jinxia & Wang, Jianzhou, 2019. "Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system," Renewable Energy, Elsevier, vol. 134(C), pages 681-697.
    15. Branko Kosovic & Sue Ellen Haupt & Daniel Adriaansen & Stefano Alessandrini & Gerry Wiener & Luca Delle Monache & Yubao Liu & Seth Linden & Tara Jensen & William Cheng & Marcia Politovich & Paul Prest, 2020. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction," Energies, MDPI, vol. 13(6), pages 1-16, March.
    16. Bangalore, P. & Patriksson, M., 2018. "Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 521-532.
    17. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
    18. Guo, Sheng & Yang, Tao & Hua, Haochen & Cao, Junwei, 2021. "Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information," Renewable Energy, Elsevier, vol. 178(C), pages 639-650.
    19. Hafiz, Faizal & Abdennour, Adel, 2016. "An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines," Renewable Energy, Elsevier, vol. 92(C), pages 136-146.
    20. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    21. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    22. Camelo, Henrique do Nascimento & Lucio, Paulo Sérgio & Leal Junior, João Bosco Verçosa & Carvalho, Paulo Cesar Marques de & Santos, Daniel von Glehn dos, 2018. "Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks," Energy, Elsevier, vol. 151(C), pages 347-357.
    23. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    24. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    25. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
    26. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    27. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    28. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    29. Sun, Gaiping & Jiang, Chuanwen & Cheng, Pan & Liu, Yangyang & Wang, Xu & Fu, Yang & He, Yang, 2018. "Short-term wind power forecasts by a synthetical similar time series data mining method," Renewable Energy, Elsevier, vol. 115(C), pages 575-584.
    30. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    31. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    32. Morshedizadeh, Majid & Kordestani, Mojtaba & Carriveau, Rupp & Ting, David S.-K. & Saif, Mehrdad, 2017. "Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production," Energy, Elsevier, vol. 138(C), pages 394-404.
    33. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    34. Zhang, Yachao & Le, Jian & Liao, Xiaobing & Zheng, Feng & Li, Yinghai, 2019. "A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing," Energy, Elsevier, vol. 168(C), pages 558-572.
    35. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    36. Nourani Esfetang, Naser & Kazemzadeh, Rasool, 2018. "A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform," Energy, Elsevier, vol. 149(C), pages 662-674.
    37. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    38. Doucoure, Boubacar & Agbossou, Kodjo & Cardenas, Alben, 2016. "Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data," Renewable Energy, Elsevier, vol. 92(C), pages 202-211.
    39. Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
    40. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    41. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
    42. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    43. Yang, Shanghui & Deng, Xiaowei & Ti, Zilong & Yan, Bowen & Yang, Qingshan, 2022. "Cooperative yaw control of wind farm using a double-layer machine learning framework," Renewable Energy, Elsevier, vol. 193(C), pages 519-537.
    44. Yin, Xiuxing & Jiang, Zhansi & Pan, Li, 2020. "Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems," Renewable Energy, Elsevier, vol. 145(C), pages 1149-1157.
    45. Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
    46. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    47. Jianguo Zhou & Xiaolei Xu & Xuejing Huo & Yushuo Li, 2019. "Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    48. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).
    49. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    50. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    51. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    52. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    53. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    54. Sizhou Sun & Jingqi Fu & Feng Zhu & Nan Xiong, 2018. "A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-21, November.
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